Cargar Datos
Paises
country_class <- read_excel("Data/CLASS.xlsx", sheet = "List of economies")
country_class <- country_class %>%
filter(!is.na(Region), !is.na(`Income group`)) %>%
merge(WDI_data$country, by.x='Code', by.y ='iso3c') %>%
select(Code, iso2c, country, Region, `Income group`) %>%
rename(iso3c = `Code`, region = Region, income = `Income group`)
V-DEM
v_dem_vars <- c("Electoral democracy index", "Exclusion by Gender index")
V_DEM <- read_excel("Data/VDEM-CORE.xlsx", sheet = "Data")
V_DEM <- V_DEM %>%
select(-3, -5, -6, -7, -8) %>%
filter(Indicator %in% v_dem_vars) %>%
rename(iso3c = `Economy ISO3`, country = `Economy Name`, indicator = Indicator) %>%
melt(id.vars = c("iso3c", "country", "indicator"), variable.name = "year") %>%
arrange(iso3c, indicator, year) %>%
merge(country_class) %>%
select(iso2c, indicator, year, value)
V_DEM$indicator[V_DEM$indicator == "Electoral democracy index"] <- "dem"
V_DEM$indicator[V_DEM$indicator == "Exclusion by Gender index"] <- "exc"
v_dem_vars <- c("dem", "exc")
HDI
hdi_vars <- c('hdi')
HDI <- read_csv("Data/datos_python_HDI.csv",
col_names = c('Code', 'iso2c', 'indicator', 'year', 'value'),
col_types = list(col_character(),
col_character(),
col_character(),
col_double(),
col_double()),
na = 'None') %>%
filter(indicator %in% hdi_vars) %>%
select(iso2c, indicator, year, value)
WORLD BANK
wb_vars <- c('ODA.GNI','ODA.ALL','ODA.PC','CC','GE','PV','RQ','RL','VA','GDP.PC','GDP.PPP','GROW','SAV')
WB <- data.frame(indicator = character(), iso2c = character(), year = double(), value = double())
for (indicator in wb_vars) {
WB <- rbind(WB, read_csv(paste("Data/datos_python_", indicator, ".csv", sep =''),
col_names = c('indicator', 'iso2c', 'year', 'value'),
col_types = list(col_character(), col_character(), col_double(), col_double()),
na = "None"))
}
WB <- WB %>% select(iso2c, indicator, year, value)
CLIO-INFRA
CLIO <- read_excel("Data/GlobalExtremePovertyDollaraDay_Compact.xlsx", sheet = "Data Long Format")
names(CLIO) <- c("ccode", "country", "year", "value")
CLIO[CLIO=="Cape Verde"] <- "Cabo Verde"
CLIO[CLIO=="Congo"] <- "Congo, Rep."
CLIO[CLIO=="Egypt"] <- "Egypt, Arab Rep."
CLIO[CLIO=="Iran"] <- "Iran, Islamic Rep."
CLIO[CLIO=="Kyrgyzstan"] <- "Kyrgyz Republic"
CLIO[CLIO=="Laos"] <- "Lao PDR"
CLIO[CLIO=="Macedonia"] <- "North Macedonia"
CLIO[CLIO=="Russia"] <- "Russian Federation"
CLIO[CLIO=="Slovakia"] <- "Slovak Republic"
CLIO[CLIO=="South Korea"] <- "Korea, Rep."
CLIO[CLIO=="Swaziland"] <- "Eswatini"
CLIO[CLIO=="Syria"] <- "Syrian Arab Republic"
CLIO[CLIO=="The Gambia"] <- "Gambia, The"
CLIO[CLIO=="Turkey"] <- "Turkiye"
CLIO[CLIO=="Venezuela"] <- "Venezuela, RB"
CLIO[CLIO=="Yemen"] <- "Yemen, Rep."
CLIO <- CLIO %>%
merge(WDI_data$country, all.x = TRUE) %>%
mutate(indicator = 'POV') %>%
select(indicator, iso2c, year, value) %>%
arrange(iso2c, year) %>%
select(iso2c, indicator, year, value)
OUR WORLD IN DATA
LIBERTIES <- read_csv("DATA/political-civil-liberties-index.csv") %>%
filter(!is.na(Code)) %>%
mutate(indicator = 'LIB') %>%
merge(country_class, by.x = 'Code', by.y = 'iso3c') %>%
select(iso2c, indicator, year, value)
Manipular Datos
Definir Índice de Governanza
datos_paper <- datos_paper %>% mutate(GOV = (CC + GE + PV + RQ + RL + VA) / 6)
Definir variables Dicotomicas / clasificacion
datos_paper <- datos_paper %>% mutate(GOV_high = case_when(is.na(GOV) ~ NA_real_, GOV >= 0 ~ 1, TRUE ~ 0))
dem_avg <- read_csv("Data/Electoral Democracy Index.csv", show_col_types = FALSE)
datos_paper <- datos_paper %>% merge(dem_avg %>% select(1, 2), by.x = 'year', by.y = 'Year', all.x = TRUE) %>%
mutate(dem_high = case_when(is.na(dem) ~ NA_real_,
is.na(`*World`) ~ NA_real_,
dem >= `*World` ~ 1,
TRUE ~ 0)) %>% select(-`*World`)
exc_avg <- read_csv("Data/Exclusion by Gender.csv", show_col_types = FALSE)
datos_paper <- datos_paper %>% merge(exc_avg %>% select(1, 2), by.x = 'year', by.y = 'Year', all.x = TRUE) %>%
mutate(exc_high = case_when(is.na(exc) ~ NA_real_,
is.na(`*World`) ~ NA_real_,
exc >= `*World` ~ 1,
TRUE ~ 0)) %>% select(-`*World`) %>%
mutate(no_exc = 1 - exc, no_exc_high = 1 - exc_high, inv_exc = 1 / exc, inv_exc_high = 1 - exc_high)
lib_avg <- read_csv("DATA/political-civil-liberties-index.csv", show_col_types = FALSE) %>% filter(Code == 'OWID_WRL')
datos_paper <- datos_paper %>% merge(lib_avg %>% select(3, 4) %>% rename(WORLD = value), all.x = TRUE) %>%
mutate(lib_high = case_when(is.na(LIB) ~ NA_real_,
is.na(WORLD) ~ NA_real_,
LIB >= WORLD ~ 1,
TRUE ~ 0)) %>% select(-WORLD)
datos_paper <- datos_paper %>% merge(country_class %>% select(iso2c, region, income), all.x = TRUE)
Filtros
## # A tibble: 3 × 2
## income countries
## <chr> <int>
## 1 Low income 12
## 2 Lower middle income 35
## 3 Upper middle income 30

## #### Filtro de países
## [1] "AF" "AO" "AZ" "BA" "BF" "BT" "BY" "BZ" "CU" "CV" "DJ" "DM" "ER" "ET" "FM"
## [16] "GD" "GE" "GN" "GQ" "GW" "KI" "KM" "KP" "LB" "LC" "LR" "LY" "MD" "ME" "MG"
## [31] "MH" "MK" "MV" "NG" "PS" "RS" "RW" "SB" "SO" "SR" "SS" "SY" "TD" "TL" "TM"
## [46] "TO" "TV" "UA" "UZ" "VC" "VU" "WS" "XK" "YE"
## #### Primer año
## [1] 1993
## #### Ultimo año
## [1] 2022
Modelos
# 1993 - 2022
datos_model <- country_class %>% filter(income != 'High income') %>% merge(datos_paper) %>%
select(iso2c, year, region, income, ODA.GNI, ODA.ALL, ODA.PC, GDP.PC, GDP.PPP,
GROW, hdi, LIB, lib_high, dem, dem_high, exc, exc_high, no_exc, no_exc_high, inv_exc, inv_exc_high) %>%
arrange(iso2c, year) %>% filter(year >= first_y, year <= last_y, !iso2c %in% filtro_c)
Índice de desarrollo humano
hdi ~ ODA.PC x exc_high + GDP.PC +
exc
OLS
| (Intercept) |
0.5045561 |
0.0062327 |
80.9526512 |
0.0000000 |
*** |
| ODA.PC |
0.0000658 |
0.0000660 |
0.9979901 |
0.3183890 |
|
| exc_high |
-0.0273137 |
0.0069231 |
-3.9452946 |
0.0000821 |
*** |
| GDP.PC |
0.0000354 |
0.0000007 |
51.0469154 |
0.0000000 |
*** |
| exc |
0.0083885 |
0.0146764 |
0.5715656 |
0.5676721 |
|
| ODA.PC:exc_high |
-0.0000928 |
0.0000762 |
-1.2179073 |
0.2233839 |
|
- R-Squared: 0.5706135
- Breusch-Pagan LM test - Cross Sectional Dependance
| 33650.77 |
0 |
2926 |
Breusch-Pagan LM test for cross-sectional dependence in
panels |
cross-sectional dependence |
- Pesaran CD test - Cross Sectional Dependance
| 42.32757 |
0 |
Pesaran CD test for cross-sectional dependence in
panels |
cross-sectional dependence |
| 2084.092 |
0 |
30 |
Breusch-Godfrey/Wooldridge test for serial correlation
in panel models |
serial correlation in idiosyncratic errors |
- Robust covariance matrix estimation
| (Intercept) |
0.5045561 |
0.0060831 |
82.9445243 |
0.0000000 |
*** |
| ODA.PC |
0.0000658 |
0.0000650 |
1.0133527 |
0.3109981 |
|
| exc_high |
-0.0273137 |
0.0068056 |
-4.0133849 |
0.0000618 |
*** |
| GDP.PC |
0.0000354 |
0.0000008 |
45.2753250 |
0.0000000 |
*** |
| exc |
0.0083885 |
0.0134999 |
0.6213764 |
0.5344134 |
|
| ODA.PC:exc_high |
-0.0000928 |
0.0000704 |
-1.3167219 |
0.1880628 |
|
Fixed Effects
| ODA.PC |
0.0001893 |
0.0000377 |
5.026430 |
0.0000005 |
*** |
| exc_high |
0.0155413 |
0.0044658 |
3.480048 |
0.0005110 |
*** |
| GDP.PC |
0.0000165 |
0.0000005 |
32.975269 |
0.0000000 |
*** |
| exc |
-0.3776387 |
0.0156980 |
-24.056493 |
0.0000000 |
*** |
| ODA.PC:exc_high |
-0.0000864 |
0.0000419 |
-2.065402 |
0.0390003 |
* |
- R-Squared: 0.5778696
- Breusch-Pagan LM test - Cross Sectional Dependance
| 31788.47 |
0 |
2926 |
Breusch-Pagan LM test for cross-sectional dependence in
panels |
cross-sectional dependence |
- Pesaran CD test - Cross Sectional Dependance
| 71.13509 |
0 |
Pesaran CD test for cross-sectional dependence in
panels |
cross-sectional dependence |
| 1703.652 |
0 |
30 |
Breusch-Godfrey/Wooldridge test for serial correlation
in panel models |
serial correlation in idiosyncratic errors |
- Robust covariance matrix estimation
| ODA.PC |
0.0001893 |
0.0000988 |
1.9147168 |
0.0556566 |
. |
| exc_high |
0.0155413 |
0.0158502 |
0.9805086 |
0.3269416 |
|
| GDP.PC |
0.0000165 |
0.0000016 |
10.5977062 |
0.0000000 |
*** |
| exc |
-0.3776387 |
0.0472271 |
-7.9962323 |
0.0000000 |
*** |
| ODA.PC:exc_high |
-0.0000864 |
0.0001142 |
-0.7571761 |
0.4490244 |
|
Random Effects
| (Intercept) |
0.6790301 |
0.0109624 |
61.941477 |
0.0000000 |
*** |
| ODA.PC |
0.0001884 |
0.0000380 |
4.951019 |
0.0000007 |
*** |
| exc_high |
0.0146207 |
0.0045069 |
3.244072 |
0.0011783 |
** |
| GDP.PC |
0.0000171 |
0.0000005 |
34.042491 |
0.0000000 |
*** |
| exc |
-0.3521546 |
0.0154305 |
-22.822041 |
0.0000000 |
*** |
| ODA.PC:exc_high |
-0.0000897 |
0.0000423 |
-2.119310 |
0.0340643 |
* |
- R-Squared: 0.5665838
- Breusch-Pagan LM test - Cross Sectional Dependance
| 32104.99 |
0 |
2926 |
Breusch-Pagan LM test for cross-sectional dependence in
panels |
cross-sectional dependence |
- Pesaran CD test - Cross Sectional Dependance
| 72.01155 |
0 |
Pesaran CD test for cross-sectional dependence in
panels |
cross-sectional dependence |
| 1736.108 |
0 |
30 |
Breusch-Godfrey/Wooldridge test for serial correlation
in panel models |
serial correlation in idiosyncratic errors |
- Robust covariance matrix estimation
| (Intercept) |
0.6790301 |
0.0102685 |
66.127685 |
0.0000000 |
*** |
| ODA.PC |
0.0001884 |
0.0000394 |
4.776484 |
0.0000019 |
*** |
| exc_high |
0.0146207 |
0.0052493 |
2.785268 |
0.0053921 |
** |
| GDP.PC |
0.0000171 |
0.0000005 |
32.137165 |
0.0000000 |
*** |
| exc |
-0.3521546 |
0.0190129 |
-18.521870 |
0.0000000 |
*** |
| ODA.PC:exc_high |
-0.0000897 |
0.0000450 |
-1.993088 |
0.0463695 |
* |
Time-Fixed Effects
| factor(year)1994 |
0.0042447 |
0.0030047 |
1.4126930 |
0.1578875 |
|
| factor(year)1995 |
0.0085316 |
0.0030082 |
2.8360736 |
0.0046090 |
** |
| factor(year)1996 |
0.0132511 |
0.0030105 |
4.4016264 |
0.0000113 |
*** |
| factor(year)1997 |
0.0175607 |
0.0030154 |
5.8236071 |
0.0000000 |
*** |
| factor(year)1998 |
0.0222234 |
0.0030188 |
7.3615516 |
0.0000000 |
*** |
| factor(year)1999 |
0.0277789 |
0.0030209 |
9.1954475 |
0.0000000 |
*** |
| factor(year)2000 |
0.0319760 |
0.0030746 |
10.3999309 |
0.0000000 |
*** |
| factor(year)2001 |
0.0375192 |
0.0030761 |
12.1971282 |
0.0000000 |
*** |
| factor(year)2002 |
0.0429128 |
0.0030864 |
13.9039773 |
0.0000000 |
*** |
| factor(year)2003 |
0.0482394 |
0.0030981 |
15.5707732 |
0.0000000 |
*** |
| factor(year)2004 |
0.0546077 |
0.0031093 |
17.5626316 |
0.0000000 |
*** |
| factor(year)2005 |
0.0613845 |
0.0031233 |
19.6540228 |
0.0000000 |
*** |
| factor(year)2006 |
0.0681522 |
0.0031386 |
21.7139058 |
0.0000000 |
*** |
| factor(year)2007 |
0.0750406 |
0.0031677 |
23.6895448 |
0.0000000 |
*** |
| factor(year)2008 |
0.0810286 |
0.0032088 |
25.2519581 |
0.0000000 |
*** |
| factor(year)2009 |
0.0874033 |
0.0031941 |
27.3641933 |
0.0000000 |
*** |
| factor(year)2010 |
0.0933284 |
0.0032703 |
28.5380830 |
0.0000000 |
*** |
| factor(year)2011 |
0.1007152 |
0.0033239 |
30.3004603 |
0.0000000 |
*** |
| factor(year)2012 |
0.1071615 |
0.0033343 |
32.1388831 |
0.0000000 |
*** |
| factor(year)2013 |
0.1134285 |
0.0033439 |
33.9209465 |
0.0000000 |
*** |
| factor(year)2014 |
0.1193723 |
0.0033483 |
35.6512280 |
0.0000000 |
*** |
| factor(year)2015 |
0.1243204 |
0.0033180 |
37.4679431 |
0.0000000 |
*** |
| factor(year)2016 |
0.1285826 |
0.0033150 |
38.7882818 |
0.0000000 |
*** |
| factor(year)2017 |
0.1326065 |
0.0033490 |
39.5960757 |
0.0000000 |
*** |
| factor(year)2018 |
0.1368097 |
0.0033624 |
40.6883332 |
0.0000000 |
*** |
| factor(year)2019 |
0.1408679 |
0.0033659 |
41.8520727 |
0.0000000 |
*** |
| factor(year)2020 |
0.1365253 |
0.0033476 |
40.7834587 |
0.0000000 |
*** |
| factor(year)2021 |
0.1343901 |
0.0033833 |
39.7212513 |
0.0000000 |
*** |
| factor(year)2022 |
0.1390266 |
0.0034073 |
40.8030966 |
0.0000000 |
*** |
| ODA.PC |
0.0000156 |
0.0000209 |
0.7485447 |
0.4542117 |
|
| exc_high |
0.0055991 |
0.0024727 |
2.2643503 |
0.0236496 |
* |
| GDP.PC |
0.0000008 |
0.0000004 |
2.1973743 |
0.0280977 |
* |
| exc |
-0.0145511 |
0.0104027 |
-1.3987824 |
0.1620193 |
|
| ODA.PC:exc_high |
-0.0000254 |
0.0000230 |
-1.1039592 |
0.2697316 |
|
- R-Squared: 0.8758466
- Breusch-Pagan LM test - Cross Sectional Dependance
| 31769.99 |
0 |
2926 |
Breusch-Pagan LM test for cross-sectional dependence in
panels |
cross-sectional dependence |
- Pesaran CD test - Cross Sectional Dependance
| -0.7670403 |
0.4430576 |
Pesaran CD test for cross-sectional dependence in
panels |
cross-sectional dependence |
| 1748.064 |
0 |
30 |
Breusch-Godfrey/Wooldridge test for serial correlation
in panel models |
serial correlation in idiosyncratic errors |
- Robust covariance matrix estimation
| factor(year)1994 |
0.0042447 |
0.0009323 |
4.5531247 |
0.0000056 |
*** |
| factor(year)1995 |
0.0085316 |
0.0013336 |
6.3974891 |
0.0000000 |
*** |
| factor(year)1996 |
0.0132511 |
0.0017433 |
7.6013312 |
0.0000000 |
*** |
| factor(year)1997 |
0.0175607 |
0.0022961 |
7.6480546 |
0.0000000 |
*** |
| factor(year)1998 |
0.0222234 |
0.0025899 |
8.5808913 |
0.0000000 |
*** |
| factor(year)1999 |
0.0277789 |
0.0031025 |
8.9537242 |
0.0000000 |
*** |
| factor(year)2000 |
0.0319760 |
0.0040211 |
7.9520993 |
0.0000000 |
*** |
| factor(year)2001 |
0.0375192 |
0.0042934 |
8.7388355 |
0.0000000 |
*** |
| factor(year)2002 |
0.0429128 |
0.0045968 |
9.3354095 |
0.0000000 |
*** |
| factor(year)2003 |
0.0482394 |
0.0049170 |
9.8107051 |
0.0000000 |
*** |
| factor(year)2004 |
0.0546077 |
0.0051848 |
10.5322831 |
0.0000000 |
*** |
| factor(year)2005 |
0.0613845 |
0.0053971 |
11.3736255 |
0.0000000 |
*** |
| factor(year)2006 |
0.0681522 |
0.0056043 |
12.1606600 |
0.0000000 |
*** |
| factor(year)2007 |
0.0750406 |
0.0058478 |
12.8322894 |
0.0000000 |
*** |
| factor(year)2008 |
0.0810286 |
0.0062050 |
13.0586257 |
0.0000000 |
*** |
| factor(year)2009 |
0.0874033 |
0.0060160 |
14.5284023 |
0.0000000 |
*** |
| factor(year)2010 |
0.0933284 |
0.0066020 |
14.1363047 |
0.0000000 |
*** |
| factor(year)2011 |
0.1007152 |
0.0067515 |
14.9174254 |
0.0000000 |
*** |
| factor(year)2012 |
0.1071615 |
0.0067516 |
15.8721225 |
0.0000000 |
*** |
| factor(year)2013 |
0.1134285 |
0.0069085 |
16.4185782 |
0.0000000 |
*** |
| factor(year)2014 |
0.1193723 |
0.0069354 |
17.2121333 |
0.0000000 |
*** |
| factor(year)2015 |
0.1243204 |
0.0067272 |
18.4801977 |
0.0000000 |
*** |
| factor(year)2016 |
0.1285826 |
0.0067062 |
19.1737640 |
0.0000000 |
*** |
| factor(year)2017 |
0.1326065 |
0.0068810 |
19.2714175 |
0.0000000 |
*** |
| factor(year)2018 |
0.1368097 |
0.0070106 |
19.5146682 |
0.0000000 |
*** |
| factor(year)2019 |
0.1408679 |
0.0070775 |
19.9034951 |
0.0000000 |
*** |
| factor(year)2020 |
0.1365253 |
0.0068482 |
19.9359559 |
0.0000000 |
*** |
| factor(year)2021 |
0.1343901 |
0.0071987 |
18.6686884 |
0.0000000 |
*** |
| factor(year)2022 |
0.1390266 |
0.0075320 |
18.4581821 |
0.0000000 |
*** |
| ODA.PC |
0.0000156 |
0.0000536 |
0.2917829 |
0.7704802 |
|
| exc_high |
0.0055991 |
0.0071156 |
0.7868761 |
0.4314392 |
|
| GDP.PC |
0.0000008 |
0.0000015 |
0.5399070 |
0.5893158 |
|
| exc |
-0.0145511 |
0.0363034 |
-0.4008193 |
0.6885921 |
|
| ODA.PC:exc_high |
-0.0000254 |
0.0000586 |
-0.4328097 |
0.6651955 |
|
Tests
- Breusch Pagan Test (heteroskedasticity)
| 72.58985 |
0 |
5 |
Breusch-Pagan test |
heteroskedasticity |
- Variance Inflation Factors - multicollinearity
| ODA.PC |
3.991936 |
| exc_high |
3.511692 |
| GDP.PC |
1.118002 |
| exc |
2.531111 |
| ODA.PC:exc_high |
4.837357 |
## Multiple parameters; naming those columns df1, df2
| 76 |
2228 |
161.7705 |
0 |
F test for individual effects |
significant effects |
| 8.396434 |
0.1356984 |
5 |
Hausman Test |
one model is inconsistent |
- Test for Time-Fixed Effects
## Multiple parameters; naming those columns df1, df2
| 29 |
2199 |
181.9916 |
0 |
F test for individual effects |
significant effects |
Unit Root Test
| 285.6322 |
0 |
154 |
Maddala-Wu Unit-Root Test (ex. var.: Individual
Intercepts) |
stationarity |
| 53.23557 |
1 |
154 |
Maddala-Wu Unit-Root Test (ex. var.: Individual
Intercepts) |
stationarity |
| 510.5379 |
0 |
154 |
Maddala-Wu Unit-Root Test (ex. var.: Individual
Intercepts) |
stationarity |
| 456.0406 |
0 |
154 |
Maddala-Wu Unit-Root Test (ex. var.: Individual
Intercepts) |
stationarity |
PIB per cápita
GDP.PC ~ ODA.PC x exc_high + GROW +
exc
OLS
| (Intercept) |
5457.31764 |
161.887045 |
33.7106509 |
0.0000000 |
*** |
| ODA.PC |
-2.99047 |
1.916982 |
-1.5599881 |
0.1189000 |
|
| exc_high |
1056.33841 |
199.249762 |
5.3015793 |
0.0000001 |
*** |
| GROW |
-663.25484 |
49.837377 |
-13.3083818 |
0.0000000 |
*** |
| exc |
-4811.03582 |
414.892071 |
-11.5958731 |
0.0000000 |
*** |
| ODA.PC:exc_high |
-2.15765 |
2.208253 |
-0.9770847 |
0.3286298 |
|
- R-Squared: 0.1693971
- Breusch-Pagan LM test - Cross Sectional Dependance
| 33552.24 |
0 |
2926 |
Breusch-Pagan LM test for cross-sectional dependence in
panels |
cross-sectional dependence |
- Pesaran CD test - Cross Sectional Dependance
| 149.8626 |
0 |
Pesaran CD test for cross-sectional dependence in
panels |
cross-sectional dependence |
| 2020.105 |
0 |
30 |
Breusch-Godfrey/Wooldridge test for serial correlation
in panel models |
serial correlation in idiosyncratic errors |
- Robust covariance matrix estimation
| (Intercept) |
5457.31764 |
231.420909 |
23.5817830 |
0.0000000 |
*** |
| ODA.PC |
-2.99047 |
2.677435 |
-1.1169159 |
0.2641467 |
|
| exc_high |
1056.33841 |
232.776891 |
4.5379866 |
0.0000060 |
*** |
| GROW |
-663.25484 |
63.605863 |
-10.4275738 |
0.0000000 |
*** |
| exc |
-4811.03582 |
427.503253 |
-11.2537993 |
0.0000000 |
*** |
| ODA.PC:exc_high |
-2.15765 |
3.011362 |
-0.7165033 |
0.4737533 |
|
Fixed Effects
| ODA.PC |
8.398996 |
1.589122 |
5.285307 |
0.0000001 |
*** |
| exc_high |
1210.928154 |
187.300267 |
6.465171 |
0.0000000 |
*** |
| GROW |
-197.056089 |
52.035842 |
-3.786930 |
0.0001566 |
*** |
| exc |
-11440.221401 |
623.051535 |
-18.361597 |
0.0000000 |
*** |
| ODA.PC:exc_high |
-5.493937 |
1.775653 |
-3.094038 |
0.0019990 |
** |
- R-Squared: 0.1690746
- Breusch-Pagan LM test - Cross Sectional Dependance
| 26960.19 |
0 |
2926 |
Breusch-Pagan LM test for cross-sectional dependence in
panels |
cross-sectional dependence |
- Pesaran CD test - Cross Sectional Dependance
| 71.62826 |
0 |
Pesaran CD test for cross-sectional dependence in
panels |
cross-sectional dependence |
| 1803.267 |
0 |
30 |
Breusch-Godfrey/Wooldridge test for serial correlation
in panel models |
serial correlation in idiosyncratic errors |
- Robust covariance matrix estimation
| ODA.PC |
8.398996 |
2.817237 |
2.981288 |
0.0029015 |
** |
| exc_high |
1210.928154 |
376.825116 |
3.213502 |
0.0013300 |
** |
| GROW |
-197.056089 |
225.433930 |
-0.874119 |
0.3821476 |
|
| exc |
-11440.221401 |
1750.670852 |
-6.534764 |
0.0000000 |
*** |
| ODA.PC:exc_high |
-5.493937 |
3.069665 |
-1.789751 |
0.0736296 |
. |
Random Effects
| (Intercept) |
6598.328413 |
334.322763 |
19.736402 |
0.0000000 |
*** |
| ODA.PC |
8.403423 |
1.583761 |
5.305992 |
0.0000001 |
*** |
| exc_high |
1220.627008 |
185.969935 |
6.563572 |
0.0000000 |
*** |
| GROW |
-225.437553 |
51.422687 |
-4.384010 |
0.0000117 |
*** |
| exc |
-10379.795742 |
586.210100 |
-17.706614 |
0.0000000 |
*** |
| ODA.PC:exc_high |
-5.790711 |
1.773181 |
-3.265720 |
0.0010919 |
** |
- R-Squared: 0.1578743
- Breusch-Pagan LM test - Cross Sectional Dependance
| 27336.98 |
0 |
2926 |
Breusch-Pagan LM test for cross-sectional dependence in
panels |
cross-sectional dependence |
- Pesaran CD test - Cross Sectional Dependance
| 80.07984 |
0 |
Pesaran CD test for cross-sectional dependence in
panels |
cross-sectional dependence |
| 1825.545 |
0 |
30 |
Breusch-Godfrey/Wooldridge test for serial correlation
in panel models |
serial correlation in idiosyncratic errors |
- Robust covariance matrix estimation
| (Intercept) |
6598.328413 |
387.297588 |
17.036844 |
0.0000000 |
*** |
| ODA.PC |
8.403423 |
1.540937 |
5.453448 |
0.0000001 |
*** |
| exc_high |
1220.627008 |
147.223052 |
8.291005 |
0.0000000 |
*** |
| GROW |
-225.437553 |
62.711540 |
-3.594834 |
0.0003314 |
*** |
| exc |
-10379.795742 |
528.675923 |
-19.633570 |
0.0000000 |
*** |
| ODA.PC:exc_high |
-5.790711 |
1.665353 |
-3.477168 |
0.0005161 |
*** |
Time-Fixed Effects
| factor(year)1994 |
48.9708478 |
177.794542 |
0.2754350 |
0.7830079 |
|
| factor(year)1995 |
181.5724476 |
177.962765 |
1.0202834 |
0.3077063 |
|
| factor(year)1996 |
247.9395504 |
178.121278 |
1.3919704 |
0.1640721 |
|
| factor(year)1997 |
255.7442672 |
178.472418 |
1.4329624 |
0.1520107 |
|
| factor(year)1998 |
210.6431927 |
178.734367 |
1.1785265 |
0.2387143 |
|
| factor(year)1999 |
197.9781734 |
178.870113 |
1.1068265 |
0.2684900 |
|
| factor(year)2000 |
294.1655935 |
181.926404 |
1.6169483 |
0.1060329 |
|
| factor(year)2001 |
261.4280169 |
182.013352 |
1.4363123 |
0.1510557 |
|
| factor(year)2002 |
235.4560185 |
182.709485 |
1.2886907 |
0.1976412 |
|
| factor(year)2003 |
402.6094652 |
183.321431 |
2.1961942 |
0.0281821 |
* |
| factor(year)2004 |
649.6634846 |
183.657002 |
3.5373739 |
0.0004125 |
*** |
| factor(year)2005 |
914.2692860 |
183.974220 |
4.9695511 |
0.0000007 |
*** |
| factor(year)2006 |
1142.2496887 |
184.281517 |
6.1983953 |
0.0000000 |
*** |
| factor(year)2007 |
1506.0265139 |
184.872673 |
8.1462906 |
0.0000000 |
*** |
| factor(year)2008 |
1934.7999586 |
185.565591 |
10.4265018 |
0.0000000 |
*** |
| factor(year)2009 |
1713.9087285 |
185.587490 |
9.2350445 |
0.0000000 |
*** |
| factor(year)2010 |
2206.7300339 |
187.849409 |
11.7473355 |
0.0000000 |
*** |
| factor(year)2011 |
2696.9244552 |
188.242967 |
14.3268272 |
0.0000000 |
*** |
| factor(year)2012 |
2790.3538992 |
188.279850 |
14.8202471 |
0.0000000 |
*** |
| factor(year)2013 |
2890.3594170 |
188.097950 |
15.3662463 |
0.0000000 |
*** |
| factor(year)2014 |
2917.6262964 |
188.140530 |
15.5076968 |
0.0000000 |
*** |
| factor(year)2015 |
2589.5987240 |
188.484521 |
13.7390525 |
0.0000000 |
*** |
| factor(year)2016 |
2533.3835536 |
188.735364 |
13.4229405 |
0.0000000 |
*** |
| factor(year)2017 |
2770.3282983 |
189.408847 |
14.6261821 |
0.0000000 |
*** |
| factor(year)2018 |
2904.9753492 |
189.338223 |
15.3427834 |
0.0000000 |
*** |
| factor(year)2019 |
2847.8067993 |
189.975605 |
14.9903815 |
0.0000000 |
*** |
| factor(year)2020 |
2540.9656729 |
191.057582 |
13.2994757 |
0.0000000 |
*** |
| factor(year)2021 |
2997.7541949 |
190.319225 |
15.7511896 |
0.0000000 |
*** |
| factor(year)2022 |
3285.0738772 |
189.896826 |
17.2992564 |
0.0000000 |
*** |
| ODA.PC |
-0.1352867 |
1.241521 |
-0.1089685 |
0.9132374 |
|
| exc_high |
607.5849699 |
145.812790 |
4.1668839 |
0.0000321 |
*** |
| GROW |
-58.5415162 |
40.248016 |
-1.4545193 |
0.1459451 |
|
| exc |
920.7303505 |
616.293474 |
1.4939804 |
0.1353242 |
|
| ODA.PC:exc_high |
-1.6346687 |
1.366878 |
-1.1959140 |
0.2318591 |
|
- R-Squared: 0.521897
- Breusch-Pagan LM test - Cross Sectional Dependance
| 42869.23 |
0 |
2926 |
Breusch-Pagan LM test for cross-sectional dependence in
panels |
cross-sectional dependence |
- Pesaran CD test - Cross Sectional Dependance
| 13.97098 |
0 |
Pesaran CD test for cross-sectional dependence in
panels |
cross-sectional dependence |
| 1802.048 |
0 |
30 |
Breusch-Godfrey/Wooldridge test for serial correlation
in panel models |
serial correlation in idiosyncratic errors |
- Robust covariance matrix estimation
| factor(year)1994 |
48.9708478 |
26.546536 |
1.8447171 |
0.0652131 |
. |
| factor(year)1995 |
181.5724476 |
54.606547 |
3.3251040 |
0.0008984 |
*** |
| factor(year)1996 |
247.9395504 |
69.578840 |
3.5634332 |
0.0003738 |
*** |
| factor(year)1997 |
255.7442672 |
80.316002 |
3.1842255 |
0.0014717 |
** |
| factor(year)1998 |
210.6431927 |
96.378416 |
2.1855847 |
0.0289512 |
* |
| factor(year)1999 |
197.9781734 |
91.687981 |
2.1592598 |
0.0309378 |
* |
| factor(year)2000 |
294.1655935 |
153.797853 |
1.9126769 |
0.0559192 |
. |
| factor(year)2001 |
261.4280169 |
147.826428 |
1.7684796 |
0.0771194 |
. |
| factor(year)2002 |
235.4560185 |
171.890756 |
1.3698004 |
0.1708891 |
|
| factor(year)2003 |
402.6094652 |
190.174888 |
2.1170485 |
0.0343677 |
* |
| factor(year)2004 |
649.6634846 |
210.427907 |
3.0873447 |
0.0020447 |
** |
| factor(year)2005 |
914.2692860 |
233.036410 |
3.9232894 |
0.0000900 |
*** |
| factor(year)2006 |
1142.2496887 |
252.524870 |
4.5233156 |
0.0000064 |
*** |
| factor(year)2007 |
1506.0265139 |
291.279744 |
5.1703785 |
0.0000003 |
*** |
| factor(year)2008 |
1934.7999586 |
333.887750 |
5.7947617 |
0.0000000 |
*** |
| factor(year)2009 |
1713.9087285 |
297.691733 |
5.7573272 |
0.0000000 |
*** |
| factor(year)2010 |
2206.7300339 |
359.993488 |
6.1299165 |
0.0000000 |
*** |
| factor(year)2011 |
2696.9244552 |
412.546929 |
6.5372550 |
0.0000000 |
*** |
| factor(year)2012 |
2790.3538992 |
420.644764 |
6.6335163 |
0.0000000 |
*** |
| factor(year)2013 |
2890.3594170 |
427.669832 |
6.7583898 |
0.0000000 |
*** |
| factor(year)2014 |
2917.6262964 |
410.741106 |
7.1033219 |
0.0000000 |
*** |
| factor(year)2015 |
2589.5987240 |
373.325279 |
6.9365748 |
0.0000000 |
*** |
| factor(year)2016 |
2533.3835536 |
356.375730 |
7.1087432 |
0.0000000 |
*** |
| factor(year)2017 |
2770.3282983 |
387.191511 |
7.1549304 |
0.0000000 |
*** |
| factor(year)2018 |
2904.9753492 |
383.967306 |
7.5656841 |
0.0000000 |
*** |
| factor(year)2019 |
2847.8067993 |
385.867495 |
7.3802713 |
0.0000000 |
*** |
| factor(year)2020 |
2540.9656729 |
371.796046 |
6.8342999 |
0.0000000 |
*** |
| factor(year)2021 |
2997.7541949 |
397.390788 |
7.5435926 |
0.0000000 |
*** |
| factor(year)2022 |
3285.0738772 |
433.305527 |
7.5814262 |
0.0000000 |
*** |
| ODA.PC |
-0.1352867 |
2.538770 |
-0.0532883 |
0.9575070 |
|
| exc_high |
607.5849699 |
394.773472 |
1.5390724 |
0.1239305 |
|
| GROW |
-58.5415162 |
165.905946 |
-0.3528597 |
0.7242275 |
|
| exc |
920.7303505 |
2003.559561 |
0.4595473 |
0.6458866 |
|
| ODA.PC:exc_high |
-1.6346687 |
2.797969 |
-0.5842339 |
0.5591229 |
|
Tests
- Breusch Pagan Test (heteroskedasticity)
| 191.0749 |
0 |
5 |
Breusch-Pagan test |
heteroskedasticity |
- Variance Inflation Factors - multicollinearity
| ODA.PC |
4.022979 |
| exc_high |
3.472398 |
| GROW |
1.070922 |
| exc |
2.414705 |
| ODA.PC:exc_high |
4.855157 |
## Multiple parameters; naming those columns df1, df2
| 76 |
2228 |
60.12209 |
0 |
F test for individual effects |
significant effects |
| 20.3481 |
0.001075 |
5 |
Hausman Test |
one model is inconsistent |
- Test for Time-Fixed Effects
## Multiple parameters; naming those columns df1, df2
| 29 |
2199 |
55.95795 |
0 |
F test for individual effects |
significant effects |
Unit Root Test
| 53.23557 |
1 |
154 |
Maddala-Wu Unit-Root Test (ex. var.: Individual
Intercepts) |
stationarity |
| 544.7777 |
0 |
154 |
Maddala-Wu Unit-Root Test (ex. var.: Individual
Intercepts) |
stationarity |
| 510.5379 |
0 |
154 |
Maddala-Wu Unit-Root Test (ex. var.: Individual
Intercepts) |
stationarity |
| 456.0406 |
0 |
154 |
Maddala-Wu Unit-Root Test (ex. var.: Individual
Intercepts) |
stationarity |
Tablas y gráficas informativas
## # A tibble: 3 × 2
## income `1993 - 2022`
## <ord> <dbl>
## 1 Ingresos Bajos 7.39e11
## 2 Ingresos Medios-bajos 1.08e12
## 3 Ingresos Medios-altos 5.23e11
## # A tibble: 6 × 2
## region `1993 - 2022`
## <ord> <dbl>
## 1 África Subsahariana 950488709675.
## 2 Asia Oriental y el Pacífico 238077840162.
## 3 Medio Oriente y África del Norte 446654290168.
## 4 Asia del Sur 303281550904.
## 5 América Latina y el Caribe 187491230376.
## 6 Europa y Asia Central 219340969717.
## # A tibble: 3 × 4
## income `1993 - 2007` `2008 - 2017` `2018 - 2022`
## <ord> <dbl> <dbl> <dbl>
## 1 Ingresos Bajos 41461879828. 128407580065. 568922430984.
## 2 Ingresos Medios-bajos 108562509747. 249514279951. 725597170717.
## 3 Ingresos Medios-altos 57709379739. 167184309642. 297975050330.
## # A tibble: 6 × 4
## region `1993 - 2007` `2008 - 2017` `2018 - 2022`
## <ord> <dbl> <dbl> <dbl>
## 1 África Subsahariana 80397939928. 213650540163. 656440229584.
## 2 Asia Oriental y el Pacífico 40231549807. 74133449865. 123712840490.
## 3 Medio Oriente y África del Norte 28067739666. 100246809658. 318339740844.
## 4 Asia del Sur 23746930111. 62526970089. 217007650704.
## 5 América Latina y el Caribe 21996269822. 46083500071. 119411460483.
## 6 Europa y Asia Central 13293339980. 48464899812. 157582729925.
fit <- lm(hdi ~ ODA.PC ,data = datos_model)
plot_ly(x = datos_model$ODA.PC, y = datos_model$hdi, type = 'scatter', mode = 'markers') %>%
add_lines(x = datos_model$ODA.PC, fitted(fit)) %>%
layout(title = 'Índice de desarrollo humano vs. ODA per cápita', showlegend = FALSE,
yaxis = list(title = 'HDI'), xaxis = list(title = 'ODA per cápita (Dolares actuales)'),
annotations = list(x = 0, y = -0.1, text = "Fuente: datos del Banco Mundial", showarrow = F, xref='paper', yref='paper'))
fit <- lm(hdi ~ GDP.PC ,data = datos_model)
plot_ly(x = datos_model$GDP.PC, y = datos_model$hdi, type = 'scatter', mode = 'markers') %>%
add_lines(x = datos_model$GDP.PC, fitted(fit)) %>%
layout(title = 'Índice de desarrollo humano vs. PIB per cápita', showlegend = FALSE,
yaxis = list(title = 'HDI'), xaxis = list(title = 'PIB per cápita (Dolares actuales)'),
annotations = list(x = 0, y = -0.1, text = "Fuente: datos del Banco Mundial", showarrow = F, xref='paper', yref='paper'))
fit <- lm(hdi ~ exc ,data = datos_model)
plot_ly(x = datos_model$exc, y = datos_model$hdi, type = 'scatter', mode = 'markers') %>%
add_lines(x = datos_model$exc, fitted(fit)) %>%
layout(title = 'Índice de desarrollo humano vs. Índice de exclusión por género', showlegend = FALSE,
yaxis = list(title = 'HDI'), xaxis = list(title = 'Índice de exclusión por género'),
annotations = list(x = 0, y = -0.1, text = "Fuente: Banco Mundial, V-Dem", showarrow = F, xref='paper', yref='paper'))
fit <- lm(GDP.PC ~ ODA.PC ,data = datos_model)
plot_ly(x = datos_model$ODA.PC, y = datos_model$GDP.PC, type = 'scatter', mode = 'markers') %>%
add_lines(x = datos_model$ODA.PC, fitted(fit)) %>%
layout(title = 'PIB per cápita vs. ODA per cápita', showlegend = FALSE,
yaxis = list(title = 'PIB per cápita (Dolares actuales)'), xaxis = list(title = 'ODA per cápita (Dolares actuales)'),
annotations = list(x = 0, y = -0.1, text = "Fuente: datos del Banco Mundial", showarrow = F, xref='paper', yref='paper'))
fit <- lm(GDP.PC ~ GROW ,data = datos_model)
plot_ly(x = datos_model$GROW, y = datos_model$GDP.PC, type = 'scatter', mode = 'markers') %>%
add_lines(x = datos_model$GROW, fitted(fit)) %>%
layout(title = 'PIB per cápita vs. Crecimiento poblacional', showlegend = FALSE,
yaxis = list(title = 'PIB per cápita (Dolares actuales)'), xaxis = list(title = 'Crecimiento poblacional (en porcentaje)'),
annotations = list(x = 0, y = -0.1, text = "Fuente: datos del Banco Mundial", showarrow = F, xref='paper', yref='paper'))
fit <- lm(GDP.PC ~ exc ,data = datos_model)
plot_ly(x = datos_model$exc, y = datos_model$GDP.PC, type = 'scatter', mode = 'markers') %>%
add_lines(x = datos_model$exc, fitted(fit)) %>%
layout(title = 'PIB per cápita vs. Índice de exclusión por género', showlegend = FALSE,
yaxis = list(title = 'PIB per cápita (Dolares actuales)'), xaxis = list(title = 'Índice de exclusión por género'),
annotations = list(x = 0, y = -0.1, text = "Fuente: Banco Mundial, V-Dem", showarrow = F, xref='paper', yref='paper'))